Video Face Swap Based on Autoencoder Generation Network
Shuqi Yan, Shaorong He, Lei Xue, Guanhua Ye, Zhifeng Xie · 2018
Video facial swap usually has strong entertainment applications, and it is also applicable for the post-production of films and has great application value. At present, the popular face swap is done manually by the PS software, and the synthetic effect of the automatic face changing technology is not good. In order to make up for the lack of these features, this paper proposes a method of video face swap based on autoencoder generation network. The network learns the mapping relationship between distorted face and original face: the encoder can distinguish and extract facial information, and the decoder can restore face separately. First, the local information of tow face is sent to the network to get the initial model; then, the global information is put into the network for fine-tuning; finally, the face exchange between A and B is completed with face alignment and alpha fusion. The experimental results show that the quality of the method is improved significantly.